Soft material has been crucial in the advancement of a wide range of applications, spanning medical and engineering applications. Finding mechanical properties of these materials is useful for many applications (Misra et al., 2008). For example, in the medical field, computational biomechanical modeling has emerged as an integral part of the advancement of several medical applications, including image guided interventions, brachytherapy, diagnostics, robotic surgery, image segmentation, and surgery training simulation. Similarly, mechanical properties of widely used soft materials, such as gel, are used in various designs and applications including, but not limited to, the design of heart valves, breathing ventilators, drug delivery, tissue engineering, and wound dressing. Many other example applications for soft materials include, but are not limited to, surgery simulators for training and tools design, the design of bio-mimicking materials, the design of vehicles for crash safety, the design of heart valves, breathing ventilators, drug delivery, tissue engineering, wound dressing, and pharmaceuticals.
Finding accurate mechanical properties is still a challenge due to the complex, expensive, unrealistic, and time consuming conventional testing procedures. For example, current biomechanical modeling primarily relies on tissue parameters based on ex vivo samples, which requires cutting, preserving, gripping and mounting the sample. In addition, these ex vivo samples exhibit substantial differences from in vivo samples, due to the effect of blood circulation, temperature, and surrounding constraints for in vivo environments (Fung 1993, Miller 2005, Kerdok et al, 2006). Furthermore, most of the existing measurements are based on a limited number of samples with a large standard deviation due to variation in experimental procedures, such as time from tissue excision, storage medium, temperature, hydration, and experimental method, in addition to a wide variation of properties between individuals.